Hepatic Gene Expression Analysis

22160 R for bio data science

Antoine Andréoletti, Olivier Gaufrès, Amy Surry, Lea Skytthe, and Trine Søgaard

Introduction

Aim: Investigating hepatic gene expression by comparing expression levels across:

- Healthy individuals - Patients with NAFLD - Patients with cirrhosis ### Data ![](doc/Pictures_for_presentation/liver_pic.png){style=“width:80%;”}
  • RNA-seq of human liver biopsies from patients with NAFLD, patients with cirrhosis, and healthy controls under both fasting and postprandial conditions
  • Meta data containing additional information about the patients

Data processing

Data processing

Methods



Workflow Diagram

Results - Descriptive analysis

Aim: First analysis of our datasets

- Distributions are fairly equal throughout our data - Sick people are older on average - No significance in disease between men and women ![](results/EDA.png){style=“width:85%”}

Results - PCA/Heatmap

Some plots

Results - DEA

Compare gene expression levels between groups using DESeq2 Output: Global Mean - Fold-change - Adjusted-p-value

Results - GSEA

Aim: Investigating hepatic gene set enrichment

- C11 hepatocytes is a subtype of hepatocytes (1 of 3) - Hepatocytes produces hepatokines (hormone involved in metabolic regulation) - Some therapies try to promote hepatocyte regeneration ![](results/GSEA/GSEA_sick_vs_healthy_patients_fasting.png){style=“width:100%”}

Discussion / Conclusion

  • Key points
  • Count_data derived from micro-array
  • Troubles with DESeq2 package and dplyr
  • Key points